Systems and methods for managing electricity supply from demand

ABSTRACT

A system to manage power consumption from a grid includes a building switchgear; an energy storage system (ESS) coupled to the building switchgear to selectively provide power in response to a customer power demand to prevent a customer grid power consumption from spiking and peaking at grid imbalance highest cost on peak times; an energy management system (EMS) to operate the ESS from behind-the-meter; and a data distribution service (DDS) coupled to the EMS forming a DDS-EMS network to provide a global data space servicing EMS edge publishers and subscribers.

This is a continuation in part application of Ser. No. 16/576,762 filed19 Sep. 2019, the content of which is incorporated by reference.

BACKGROUND

The present invention relates to managing electric power costs,particular those in a time of use (TOU) environment with QualifiedBalance Resources (QBR).

Lowering utility bill and Using clean energy are two major goals forenergy market. In spite of great efforts of lowering cost of energy,often referred to Levelized Cost of Energy or LCOE), for solarphotovoltaic and other renewable energy resources, utility bill has keptincreasing to the rate payers. One of the major causes of increasingenergy rates is due to imbalance of grid in terms of Time-of Use. Forexample, in California Grid, prices of energy have dramaticallyincreased during the evening hours with the high demand over past years,known as “Duck Curve.” Unfortunately, thermal generators, known as “gaspeakers,” are largely used to mitigate this high demand of the eveninghours due to their dispatchability. As a result, rate payers have to useunclean energy and pay higher utility rates from the grid.

In recent, energy policy makers have driven energy storage resources,mainly consisting of battery energy storage system, to participate ingrid balancing as an alternative solution to lowering the dependency ofthermal generators. FERC Order 841 and FERC Order 2222 allows energystorage resources to participate in grid wholesale's trading marketincluding aggregation from Behind-the-Retail Meter resources. Facinghigh costs in new power plants, advancements in energy technologies,battery storage and consumer cost-cutting programs such as net-meteringincentives, many local utilities need to adjust their business models sothey continue to be profitable and relevant in the country's energysystem.

One adaptation that utilities nationally have begun to implement istime-of-use (TOU) pricing, which includes demand charges for mostbusiness power consumers. Local utilities in California, Arizona,Massachusetts and other states have adopted TOU pricing as a means notonly to combat falling revenue but also to ensure that they have thenecessary finances to keep the energy grid running.

Time-of-use is a rate plan in which rates vary according to the time ofday, season, and day type (weekday or weekend/holiday). Higher rates arecharged during the peak demand hours and lower rates during off-peak(low) demand hours. Rates are also typically higher in summer monthsthan in winter months. This rate structure provides price signals toenergy users to shift energy use from peak hours to off-peak hours.Time-of-use rate plans better align the price of energy with the cost ofenergy at the time it is produced. Lower rates in the winter and duringpartial-peak and off-peak hours offer an incentive for customers toshift energy use away from more expensive summer and peak hours, whichcan help consumers save money and reduce strain on the electric grid.

Currently, all commercial, industrial and agricultural customers inCalifornia are required to be on a time-of-use plan. If customers haveenergy usage that can be shifted from peak hours to off-peak hours, theymay be able to reduce their energy bill by switching to a time-of-userate plan. Some time-of-use rate plans include a monthly Demand Charge.This Demand Charge is calculated by using the 15-minute interval duringeach billing month when a business uses its maximum amount ofelectricity. As a benefit to this type of rate plan, regular electricityusage charges are about 30% lower than for a comparable rate planwithout a Demand Charge, and the Demand Charge depends on a customer'speak monthly demand patterns.

One way to lower monthly Demand Charge is to stagger the times ofequipment operation, rather than using all equipment at the same time,minimizing spikes in your electricity use. spread your electricity usethroughout the day to lower the Demand Charge. However, businessesfrequently cannot keep constantly monitoring their equipment powerusage, and as a result, many businesses face high electricity bills.

The grid operation and balancing of electricity pricing to keep theelectric grid energized is heavily relied on the available baseload andpeak power generations suppling through grid intertie locations everyhour of the day to serve load. This method ensures the reliability andsafety for electricity consumption; however, requires electricity demandof customer's usage behavior to be in similar patterns throughout thegrid every 5 minute, 15 minute, and hourly times throughout a 24 hourperiod and the extra setup of supply reserves for when load demand usagepatterns change. With aging grid lines and baseload generationsrequiring more costly supply reserves bundled with uncontrollablerenewable power generation interconnecting into the grid, patterns arechanging thus causing more issues triggering high cost of electricity.

SUMMARY

In a first aspect, a system to manage power consumption from a gridincludes a building switchgear; an independent system operator (ISO)meter coupled to the building switchgear, the ISO meter including atelemetry unit to communicate with an ISO; and an energy storage system(ESS) coupled to the building switchgear, wherein the ESS selectivelyprovides power in response to a customer power demand to prevent acustomer grid power consumption from spiking and peaking at gridimbalance highest cost on peak times.

In a second aspect, a system to manage power consumption from a gridincludes a utility meter coupled to the grid and site switchgearproviding historical interval data; a site meter behind the utilitymeter (BTM) coupled to the switchgear; an independent system operator(ISO) meter coupled to the BTM switchgear; a telemetry unit tocommunicate with an ISO; and an energy storage system (ESS) coupled tothe telemetry unit, switchgear, and ISO allowable resource performancemeter, wherein the ESS selectively provides power in response to acustomer power demand to prevent a customer grid power consumption tospike at the time when cost is more expensive (“congested hours”) on themain grid to provide the high usage without disruption to grid powermanagement and customer's business operation.

In a third aspect, a method to manage power consumption from a gridincludes profiling customer electricity usage and illustrating potentialcost savings; optimizing a resource capacity of equipment based on theprofile, wherein the equipment includes ESS or ESS coupled withalternative generation such as Solar, separate meters for utility,Regional ISO, resource performance, and for NOC control of equipment;and manage the equipment to minimize cost for customer.

In a fourth aspect, a site switchgear includes a first connection for autility meter providing historical interval data; a second connectionfor a site meter behind the utility meter (BTM), a third connection foran independent system organization (ISO) meter, and a fourth connectionfor an energy storage system (ESS) that selectively provides power inresponse to a customer power demand to prevent a customer grid powerconsumption to spike at the time when cost is more expensive on the maingrid to provide the high usage without disruption to grid powermanagement and customer's business operation.

In a fifth aspect, a system to manage power consumption from a gridincludes a utility meter coupled to the grid and site switchgearproviding historical interval data; a site meter behind the utilitymeter (BTM) coupled to the grid switchgear; an independent systemoperator (ISO) meter coupled to the BTM grid switchgear; a telemetryunit to communicate with an ISO; and an energy storage system (ESS)coupled to the telemetry unit, switchgear, and ISO allowable resourceperformance meter, wherein the ESS selectively provides power inresponse to a customer power demand to prevent a customer grid powerconsumption to spike at the time when cost is more expensive on the maingrid to provide the high usage without disruption to grid powermanagement and customer's business operation.

In a sixth aspect, each building site that is equipped with QBR becomesa grid addition serving the balance of supply and demand fromconsumption usage needs that synchronizes with the grid TOU.

In a seventh aspect, a system to manage power consumption from a gridincludes a building switchgear; an energy storage system (ESS) coupledto the building switchgear to selectively provide power in response to acustomer power demand to prevent a customer grid power consumption fromspiking and peaking at grid imbalance highest cost on peak times; anenergy management system (EMS) to operate the ESS from behind-the-meter;and a data distribution service (DDS) coupled to the EMS forming aDDS-EMS network to provide a global data space servicing EMS edgepublishers and subscribers.

Advantages of the above aspects may include one or more of thefollowing. The system mitigates the cost for both grid and customers isto reverse some of the grid balancing reliance from power energy supplyside to demand load side. As each commercial, industrial, andagricultural customer's demand and energy usage are being balanced andmanaged using the power consumption management method stated above, themeter(s), resource equipment, and load data of customer's time of use ofgrid power versus the time of use of the optimized and controllableresource capacity are then networked in an aggregated energy pool withlocation identification like a map providing utilities and gridoperators real-time information of reliable and available energy atspecific times. This synchronization with grid operation method reducesthe need to rely on excess and expensive power supply for grid operatorswhile reducing fuel costs for power producers with more accuracy on loadcapacity needs.

Other advantages may include one or more of the following. Buildingsequipped with QBR capable systems helps to defer/reduce the upgrade costfor utilities because the QBR system better matches demand with supplyand reduces the extra capacity that the grid must hold in reserve forpeak power consumption. As the system incorporates a high degree ofcontrol for all parties, grid operators can decrease/defer the cost ofupgrading grid capacity. An aggregation of smart switchgears inbuildings turn the buildings into smart powerplant, further reducingutility upgrade costs.

Certain Definitions

Unless otherwise defined, all technical terms used herein have the samemeaning as commonly understood by one of ordinary skill in the art towhich this invention belongs.

As used herein, the singular forms “a,” “an,” and “the” include pluralreferences unless the context clearly dictates otherwise. Any referenceto “or” herein is intended to encompass “and/or” unless otherwisestated.

As used herein, the term “about” refers to an amount that is near thestated amount by about 10%, 5%, or 1%, including increments therein.

As used herein defined, the term ISO meter (such as those for CAISO) aremeters that conform to the following properties:

ISO meter should pass the NIST Traceable Laboratory testing whengeneration of Settlement Quality Meter Data (SQMD) direct with ISO

ISO meter should have at least 2 channels, preferably 4 and is capableof measuring both load and generation when generation of SQMD is fromISO certified Scheduling Coordinator (SC)

Should have the minimum of the following technical specificationstandards:

ANSI C12 along with other UDC and LRA requirements at time of install

ANSI C12.1—American National Standard Code For Electricity Metering

ANSI C12.6—American National Standard For Marking And Arrangement OfTerminals For Phase-Shifting Devices Used In Metering

ANSI C12.7—American National Standard For Watt-hour Meter Sockets

ANSI C12.8—American National Standard For Test Blocks And Cabinets Forinstallation Of Self-Contained A-Base Watt-hour Meters

ANSI C12.9—American National Standard For Test Switches ForTransformer-Rated Meters

ANSI C12.10—American National Standard For Electromechanical Watt-hourMeters

ANSI C12.11—American National Standard For Instrument Transformers ForRevenue Metering, 10 kV BIL Through 350 kV BIL

ANSI C12.16—American National Standard For Solid-State ElectricityMeters

ANSI C12.18—American National Standard For Protocol Specification ForANSI Type 2 Optical Port

ANSI C12.20—American National Standard For Electricity Meters 0.2 and0.5 Accuracy Class

ANSI C57.13—IEEE Standard Requirements for Instrument Transformers

Revenue quality with a 0.2 Accuracy Class

Remotely accessibly, reliable, 60 Hz, three phase, bi-directional,programmable and multifunction, and certified for correction operationat the service voltage. If single phase connection then metering devicerequirements should meet utility distribution companies.

Capable of measuring kWh, kVARh, and providing calculated three phasevalues for kVAh, kVA

Should have demand function including cumulative, rolling, blockinterval demand calculation and maximum demand peaks

Should be battery backup for maintaining RAM and a real-time clockduring outages of up to 60 days.

Should be capable of being powered either internally or externally froman AC source. It is recommended that all meters have an auxiliary sourceor emergency backup source of power to avoid loss of data.

Should be capable of providing data to the data collection system usedby the Scheduling Coordinator.

Should be capable of providing interval data at granularity requiredbased on market participation.

Should be capable of 60 days storage of kWh, KVARh, and/or 4 quadrantinterval data

Should be calibrated to provide the following accuracy:

-   -   0.2% at full load at power factor of 100%;    -   0.25% at full load at power factor of 50% lag;    -   0.25% at full load power factor at 50% lead; and    -   0.25% at light load at power factor of 100%.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an exemplary process for optimizing electricity cost forpower consumers.

FIGS. 2A-2C illustrate an exemplary on-line user interface to illustrateelectricity cost analysis and recommendations for a customer.

FIGS. 2D-2E illustrate an exemplary electricity costs analysis andrecommendation for a customer.

FIGS. 3A-3B illustrate in more detail operation 2 of FIG. 1 , while FIG.3C illustrates an exemplary cost saving projection.

FIG. 4 shows an exemplary hardware formed using the capacity sizingsolution in the process of FIG. 1 .

FIG. 5 shows in more details one implementation of FIG. 4 .

FIG. 6 shows an optional PV module that is connected to the ESS of FIG.4 .

FIG. 7 shows an exemplary cloud based energy management system.

FIG. 8 shows an exemplary Data Distribution Service (DDS) system.

FIG. 9 shows an exemplary scenario with a DDS-based EMS.

DESCRIPTION

FIG. 1 shows an exemplary process for optimizing electricity cost forpower consumers. The process starts by profiling the customerElectricity Usage using historical 15 min interval data gathered fromutility GB data sharing or utility interval data files, and suchinformation is used to illustrate potential approaches for cost savings(2). The result is rendered as an online service as illustrated in theexemplary user interface of FIGS. 2A-2B, and a report can be generatedas illustrated in FIG. 2C. Next, the process optimizes Resource Capacityof Equipment based on Profile and Design Equipment including ESS or ESS+Alternative Generation with the Capacity (4).

The equipment is enhanced with separate meters, one for the load, onefor the Regional ISO (such as the California Independent SystemOperator), and one for the network operations center (NOC) controllerand for local control of equipment (6). The NOC manages the equipment tominimize cost for customer (8) and to provide on-site availability ofPower to Grid on Demand (10).

As the NOC controls a large number of distributed equipment that canprovide precise available power for the grid operation on demand, theNOC acts as virtual power plant whose power can be drawn on-demand overa selected period to avoid high costs and electricity losses of peakingplants while assisting in the relief of congestion interties ofelectricity transporting through the grid, for example.

FIGS. 2A-2B illustrates an exemplary on-line user interface toillustrate electricity cost analysis and recommendations for a customer,while FIG. 2C illustrates an exemplary electricity costs analysis andwhat will be provided by the system for a customer site.

Turning now to FIGS. 2A-2C, the set of User Interfaces consists of,

-   -   a) Logging in with user name and Id.    -   b) Click on ESSEmulator.    -   c) Select customer's profile from the list.    -   d) Select from ‘Validate Customers Profile (VCP)’, ARES™ (50,        75, 95), Conventional Solar.    -   e) Click on customer load graph to view Monthly to Daily, Daily        to 15 Min interval.    -   f) Click ‘Back’ button to view 15 Min interval to Daily, and        Daily to Monthly.    -   g) Click ‘Back to all’ button to view 15 Min interval or Daily        to Monthly Graph.

Customers who are registered with the system and connected with 15 mininterval data such as Green Button to the system database will beprofiled in the savings simulator. Utility Tariff Rate Schedule, energyconsumption pattern will be displayed.

The system operator can select Customer from the list, and the site loaddata, and tariff rate schedule will be automatically displayed. Next,QBR Analysis software will be providing optimal input capacity bylooking at the highest peak and lowest peak during the peak hours. The95 percent of lowest peak, and 50% of the highest peak. Whichever haslower value will be the optimal input capacity. The software willdisplay demand cut (50%, 75%, 95%) on the customers load graph duringhighest peak hours (4 pm to 9 pm). The optimized scenario adjusts systemoutput so that the lowest power consumption is at zero. The savingscaused by Energy Storage System will be generated and displayed. Thesoftware can display the highest peak date graph with 50%, 75%, 95%, andoptimized graph of highest peak during peak hours. That will be used assystem input capacity and will display demand cut by the input capacityduring the highest peak (4 pm to 9 pm) and savings will be generatedbased on the amount cut and tariff schedule. A conventional SolarAnalysis can also be done to show the difference between traditionalsolar on demand/usage at site versus AERS™ optimum with or withoutsolar, where the software will display solar kWh cut on the customersload graph during sun hours (11 am to 5 pm). The solar input and outputdata are based on NREL PVwatt calculator where QBR analysis systemconnects directly with through API. A visual of traditional and optimumof solar kWh cut will be displayed on the customers load graph duringthe sun hours and highest peak expensive hours of grid. Using customer'saddress information, NREL API will provided the solar input and outputdata to generate the enhanced QBR ESS+Solar system projection data.

As shown in FIGS. 2D-2E, QBR savings analysis report for ESS+Solarspecializes in analyzing optimum system capacity based on load analysisof site for the purpose of reliability, congestion relief, costreduction and deferral needs of grid operation rather than thetraditional increasing of solar system capacity and alteration ofutility rate tariff to accommodate grid balancing. Solar generation canthen significantly be in a real-time controllable environment to beutilized at the proper times of load usage and grid needs with lesscosts for customers, utility and grid operators.

FIGS. 3A-3B shows in more details the profiling operation 2 of FIG. 1 .Turning now to FIG. 3A, more details are provided for determining theclient profile. First, the process obtains a Validated Customer/ClientsProfile (VCP). The customer authorizes and provides 15 min intervaldata, and in one embodiment with PG&E as the utility, this is done via aGreen Button connection (“Validation Process”). The most recent one-yeardata of the customer's consumption behavior is digitized byyearly/monthly/daily patterns to find the Demand Peak Patterns and thePatterns during ON PEAK hours under Utility Tariffs. Typically theutility and grid operators provides 2-3 times more demand capacity thanactual usage of clients. This causes significant “waste” byoversupplying energy, transmission delivery capacity (T-Demand) anddistribution delivery capacity (D-Demand). The system performssynchronizing of customer's pattern into Grid operation, which is theultimate goal of “Balancing.”

Turning now to FIG. 3B, the process of emulating and optimizing BTMResources and Savings is detailed. An Emulator calculates and optimizesthe capacity of BTM resources which can be integrated with EnergyStorage System (ESS) and Solar photovoltaic (PV) cells. Oneimplementation of the Emulator runs the following pseudocode:

-   -   Find the highest peak (kW) during ON PEAK hours (highest        electricity price by congested pricing)    -   Find the lowest peak (kW) during ON PEAK hours    -   Calculate both monthly (seasonal effects are counted) and daily    -   Calculate 95% of lowest peak and compensate with highest peak        with compensators of 50%, 75% and 95%, and optimized (or lowest        consumption at zero)

As an example, a Client with 1,000 kW (highest peak) and 800 kW (lowestpeak during ON PEAK hours)

-   -   Optimum ESS Capacity: 800 kW×95%=760 kW compensated with 76% of        highest peak    -   Client's utility ON PEAK hours: 5 Hours    -   Total Storage Capacity=760 kW×5 hours=3,800 kWh    -   System Configuration of ESS: 760 kW (PCS)+3,800 kWh        (Li-Battery)*—will be further adjusted by ex-factory hardware        standard capacity (“name plate capacity”)    -   Solar PV uses ONLY TO FEED BATTERY (i.e., NO A/C connection to        Grid)    -   Customer's site sun radiation hours: 6 hours (example)    -   Maximum capacity of energy production from Solar: 3,800 kWh        calculated by battery capacity    -   3,800 kWh×365 days/year=1,387,000 kWh/year    -   1,387,000 kWh to reverse calculated PV panel capacity from NREL        (PV watts calculator): 1,387,000/6 hours/82% (NREL)/365        days=772kWdc of Solar PV required    -   *760 kW PCS+3,800 kWh Li-Battery+772kWdc Solar PV are optimized        to realize THE MAXIMUM VALUE OF ENERGY to balance rate and        balance grid

FIG. 3C shows an exemplary cost saving projection. In one embodiment,QBR Integration CONTROLS ENERGY AND TIME. QBR delivers precise amount ofenergy at the best time which often occurs highest price due tocongestion of demand. QBR Integration saves BOTH DEMAND CHARGE ($/kW)and ENERGY CHARGE ($/kWh). QBR can precisely reduce Demand Peak andshows approximately 3 time more savings than that of conventional solarsaving projections

AERS™ QBR system integration designs with existing infrastructure inmind to help utilities, state, and authorized local jurisdiction (ALJ)defer and/or reduce the cost of upgrades and improvements to accommodatesocietal changes such as population/development growth, climate, and/orincreased electrical connecting device lifestyles by balancing theelectricity usage demand synchronized with grid operation balancing24/7/365.

Conventional solar system and its capacity is designed based on fix time(NREL sun hours) and financial attributes for the solar system energygeneration itself such as utility solar tariffs which are normallycapped for retail rates (non-export connection) or using variable hourlywholesale rates (export connection). In other words, the solar system isnot controllable and/or synced with grid balancing operation. Therefore,when highest cost of on-peak hours changes with utility and gridbalancing operation of physical electron supply and demand the solarpower generation from the conventional system becomes part of the“wasted” power supply in the energy transportation chain and requiresmore expansive peaking power generation needed for grid stability. Byusing AERS™ Optimum QBR system configuration of ESS+Solar three thingsoccur: 1) equipment costs of system integration reduces as solar becomesa DC coupled system directly into ESS power conversion system (PCS),therefore solar invertors are eliminated; 2) since solar is designedmainly for the purpose of assisting the energizing of ESS, systemcapacity of solar will never be more than ESS capacity and physicalelectrons does not directly connect to load usage directly; 3)controlled time operation of solar electron usage, which can be mostlydispatched accordingly on the hours of most needed times for both gridand usage demand, thus no alterations of utility tariff rate schedulesis necessary and the dependency of sun hours can be irrelevant for gridoperation.

FIG. 4 shows an exemplary system designed in FIG. 1 . In this system,power is supplied by the grid and consumed by one or more loads. Suchconsumption is measured by a utility meter 102 and a site meter 104, anda performance meter 106. Data captured by meter 106 is provided to atelemetry unit 108 that provides to an ISO/utility authorizedcommunication protocol 110. The output of telemetry unit 108, along withthe site meter 104, is provided to an energy storage system (ESS)controller 116. The controller 116 also receives line quality data ascaptured through protective relays 114. The controller 116 also controlsHVAC systems, fire alarms, alert signal systems, and/or suppressionsystems, sensors, and input/output devices 122. The controller 116 alsocontrols a battery system 124 with a battery management system and aplurality of battery racks. The controller 116 can control the chargingof the battery system 124 using a power conversion system 126, which hasa DC disconnect 128 for safe disconnect from the battery system 124.Similarly, an AC disconnect 130 is positioned between grid power and asecond AC disconnect 132 before power goes into the PCS 126.Additionally, other PCS systems or battery systems 140 can be connectedto the output of the AC disconnect 130.

The ESS 116 selectively provides power in response to a customer powerdemand and energy usage behavior to prevent a customer grid powerconsumption from high spiking peaks during the grids most unstable orimbalanced high cost times. For the majority of AERS™ QBR operation, thecustomer's power consumption is well within the utility and gridoperations baseload supply thus keeping the electric bill at the lowestcost possible. During the off-peak hours usually the baseload's low-costrate period, the ESS is charged or energized from the grid power some orall of energy needed depending on QBR ESS or ESS+alternative powergeneration system installed on site. The increase of site loads off peakcost hours are minimal if any because discharging hours of QBR ESS forhigh cost on peak hours are mainly 6 hours or less accumulated in a 24hours period and the lowest cost hours for charging can be spreadthrough efficiently through a spread of the rest of 18 hours.

As the ESS 116 only kicks in on a minority of the time, the ESS 116contains power that can be tapped into to correct grid disturbances.This ability is enhanced when aggregation of ESS 116 connected at C&Imain electric switchgears that can be controlled by a network operationscenter (NOC) to collectively supply power into the grid by dischargingfor reduction of load from grid or by charging to increase loadconsumption when grid is over energized to address a power imbalancethat can lead to brown-outs. When such collection of ESSes provide powerto the grid, they can be compensated by the utility or ISO. The utilitywins because it can avoid spending billions on a new powerplant, and theESS/NOC wins with extra revenue from being a virtual power plant thatcan inject or reduce power for a selected period in response to arequest from an ISO or a utility. Thus, the meters need to be ISOallowable and/or revenue grade meters.

In the system of FIG. 4 , the utility meter and the ISO meter arerevenue grade meters that conform to specifications by the utility andthe ISO. Meter data represents the energy generated or consumed during asettlement interval. The ISO, ISO metered entities, and schedulingcoordinator (SC) metered entities follow prescribed processes andprocedures to ensure the data is settlement quality. The ISO meterperforms accurate metering of electricity generated or consumed provideskey data inputs for accurate settlement calculations. Direct measurementof a generator or load participant through telemetry allows the ISO orthe utility to manage and monitor power generation in real-time. Thespecification of the meter is highly controlled, as the ISO and utilitybill based on the meter output.

In one embodiment, CAISO Metered Entities ensure that the Meter Dataobtained by the CAISO directly from their revenue quality meters is raw,unedited and un-aggregated Meter Data in kWh values. The CAISO or SCwill be responsible for the Validation, Estimation, and Editing processof that Meter Data in order to produce Settlement Quality Meter Data.

The system of FIG. 4 conforms to utility and ISO specifications, as theISO controls the local utilities to ensure orderly operation forelectricity supply in a region. For example, the California IndependentSystem Operator (CAISO) is a non-profit Independent System Operator(ISO) serving California and oversees the operation of California's bulkelectric power system, transmission lines, and electricity marketgenerated and transmitted by its member utilities. By providing aseparate compliant meter for the ISO, the system can now participatedirect to the ISO to help facilitate local utility's reliability andCAISO's grid balancing of energy supply and demand. Each QBR resourceare registered with CAISO SC resource ID and AERS™ Point ofControl/Trade (POC/POT) ID that are synchronized with utility ratetariff, system load meter data, and CAISO Lap Points and pNodes.

FIG. 5 shows in more details one implementation of FIG. 4 . In thisembodiment, a meter and a NOC network controller are connected to apower management system (PMS). The PMS in turn is connected to each ofthe aggregated ESS management system that are connected a remote on siteNOC controller. Each BMS/PCS combination is tied to manual stop buttonor disconnect switches and circuit breakers on site to ensure safety andsecurity of onsite system. Also, to ensure proper metering telemetry andthe safety of physical electricity connection, the QBR system it isprotected using protective relays approved by utilities. In turn each ofthe QBR POC satisfies the minimum requirement of California PublicUtilities Commission Electrical Interconnection Tariff Rule 21.

The charging and discharging scheduling method for ESS in FIG. 4-5 undertime-of-use price applied in one embodiment, accesses the ESS as part ofelectricity grid device to safely and efficiently deliver electricity toand from buildings, that plays a role of load shifting, improves thesafety and stability of the power and energy usage operation undertime-of-use price, and meanwhile increases the efficiency of energyutilization and the economy of the transmission and distribution gridand load usage operation, that can truly make building-to-grid (B2G)feasible and controllable building demand into grid assets.

The charging and discharging scheduling method for the system of FIG.4-5 under time-of-use price applied in one embodiment, incorporates notonly the ESS, but also the photovoltaic unit, and other power sourcessuch as gas generator, into the optimal scheduling model of B2G, whichconsummates the optimal scheduling model considering only the ESS.

FIG. 6 shows an optional PV module that is connected to the ESS of FIG.4 . In this embodiment, the PV module provides power to the ESS that isthen smoothed and used and/or stored by. As power is needed through thebalancing of the grid operation chain, building's load usage can thenprovide and facilitate through the control of QBR systems. In anotherperspective the building's switchgear is upgraded to become a smartswitch by adding on QBR attributes as a behind-the-meter resource forutilities and grid operators without the actual upgrade costs and timeconstraints. In this embodiment, a combiner collects outputs from the PVarrays, and the collective PV outputs are provided directly to a DC toDC coupled PCS connected to storage unit. When needed, the ESS drives aninverter to generate AC outputs, thus eliminates electricity loss orwaste of generation. One embodiment runs the following determinations:

a. winter_energy_arbitrage=winter_partpeak_energy−winter_offpeak_energy

b. summer_energy_arbitrage=summer_maxpeak_energy−summer_partpeak_energy

c.arbitrage_avg_rate=(winter_energy_arbitrage*8+summer_energy_arbitrage*4)/12

d. energy_avg_rate=(winter_partpeak_energy*8+summer_maxpeak_energy*4)/12

e. Solar_saving_DC_yr1=Summer_Maxpeak_demand*Input_capacity*4

f. ECM_Saving_yr(n)=energy_avg_rate*Input_capacity*5*365*(1.05)n−1

g. Total_saving_yr(n)=Solar_saving_DC_yr(n)+ECM_Saving_yr(n)

h. WO_soalr_Total_yr(n)=Solar_saving_DC_yr(n)+WO_solar_ECM_yr(n)

i. HoursFilterData={A⊆Data: Data is in between 4 pm to 9 pm}

j. AERS

i. AERS_power_base=min(HoursFilter(daily_max)*0.5,HoursFilter(daily_min)*0.95)

ii. AERS_50=AERS_power_base*0.5

iii. AERS_75=AERS_power_base*0.75

iv. AERS_90=AERS_power_base*0.95

v. AERS_OPTIMUM=For all energy usage between 4 pm to 9 pm, subtractdaily_min

k. Conventional Solar

.PvWATT is an API call accepts parameters including input capacity,address, and so on.

i.sunhours=PvWATT(input, address)/input/30

In this embodiment, the customer's consumption history data isautomatically download from Utility Servers, called “GREEN BUTTON.” Anemulator calculates and computes lowest peak data during TOU-ON PEAKwith highest peak one per yearly, monthly and daily out of thecustomer's history data. The emulator computes OPTIMUM capacity ofresources, such as Energy Storage System, Solar PV and Gas Generator, inorder to maximize economy value of the resources. The OPTIMUM CAPACITYvalue generates economy projections over 20-project years. VCP is arequirement in order to apply for California SGIP incentives program.Based on VCP, the system provides the fully TOU synchronized systemdesign, called Qualified Balance Resources (QBR). QBR providesDefinitive Capacity of Resources made by one or multiple integrationsfrom Energy Storage System, Solar PV and Gas Generator as well as Gridpower. The capacity from each resource shall be computed andsynchronized by TOU patterns of the users and GRID.

FIG. 7 shows an exemplary web architecture for the cloud based energymanagement system. The system includes web/html clients that communicateover http channels to an application server with an Application ProgramInterface (API). The server and API handler communicates with a databaseserver that responds with data upon request.

The AERS technology is applied with an energy management system (EMS) tooperate energy storage resources from Behind-the-Retail Meter. The EMSsystem exists at each end, which plays the role of organicallycontrolling and monitoring terminals such as relays, meters, BMS, andPCS. In the past, devices similar to EMS existed, but it was composed ofa traditional server-client structure, having potential problems. Theserver-many client model has the following problems. First, the state ofthe server affects the entire system. Since the server has to handlereal-time responses from multiple clients, the load is always high, andthe server system down due to this high load is fatal to the system'sreliability. Second, it is about the scalability of the server. Thetraditional server-client architecture makes it difficult to expand theserver to support more clients. Third, servers are always vulnerable tohacking, such as attacks from hackers and malware attacks. Due to theabove problems, it is not easy or impossible to implement a safe,reliable, and available system through traditional methods. An EMS tracesubsystem with transceivers communicate with the EMS global data spaceto provide run-time verification.

To address the above issues, FIG. 8 shows an exemplary Data DistributionService (DDS) system. AERS embraces a distributed network's advantages,providing a global data space, quality of service (QoS), filtering,dynamic discovery, scalable architecture, and enhanced security as shownin FIG. 8 . In this embodiment, a plurality of EMS edge publishers (orwriters), each conforming to QoS requirements, communicate wirelesslyover a cloud to an EMS global data space that includes BMS, Meter, PCS,and control software. A plurality of EMS edge subscriber devices (orreaders) receive data from the EMS global data space.

The DDS is a state-of-the-art methodology/technology in which each nodecan exist independently and, at the same time, perform informationexchanges. Also, AERS has an additional layer that guaranteestraceability and a response within 1000 ms, making it possible to ensurereal-time, which is significant in the energy market. Through this, EMSdevices of AERS, which are distributed everywhere, search/build networkswith each other, and in the event of a failure, they can perform safedata exchange without affecting other EMSs.

The energy market is changing from a large power plant to numerous smallvirtual power plants. These changes are challenging to cope withtraditional system architecture/techniques, and AERS proposes areal-time, traceable system based on DDS.

FIG. 9 shows an exemplary scenario with a DDS-based EMS. FIG. 9 shows asequence diagram showing a scenario in which a DDS-based EMS works. TheEMS Global Space is not a physical but logical domain in thisembodiment, having a distributed network. Each EMS Edge first looks forthe DDS-EMS network. Each node receives an acknowledgment (ACK) andreceives a signal that it has successfully connected. EMS Trace recordsall actions that occur (black box), which is passed on to the RuntimeVerification (RV) unit. Each edge performs read/write according to theQoS, and when it violates the 1000 ms operation time limit, which isglobal QoS, it also informs the trace. EMS Trace converts the recognizedsignal into a well-defined property and delivers it to the RV, and theRV performs runtime monitoring based on Linear Temporal Logic (LTL).This RV is an in-situ middleware that always checks/monitors the overallsafety of the system. Pseudo-codes for the modules in FIG. 9 include:

Writer of EMS edge Module or Node

01 With DDS.interface.open connector(Participant, SubParticipant) asconnector

02 Output=connector.get_output( )

03 Topic.register(“EMSData”)

04 Output.wait_for_subscription(Topic)

05 While(true)

06 (Meter, PCS, BMS, Relay)=getEMSEdgeData( )

07 Output.setData(Meter)

08 Output.setData(PCS)

09 Output.setData(BMS)

10 Output.setData(Relay)

11 Output.write( )

12 Wait(1000 ms−time taken for getEMSEdgeData( )−jitter)

Pseudo code: EMSTrace Module or Node

01 With DDS.interface.open connector(Participant, SubParticipant) asconnector

02 Input=connector.get_input( )

03 Output=connector.get_output( )

04 Topic.register(“EMSData”, “Trace”)

05 Input.wait_for_publication(Topic[0])

06 Output.wait_for_subscription(Topic[1])

07 While(true)

08 Input.wait( )

09 Input.take( )

10 Array of microOperations=convertToMicroOperations(Input.instance.get())

11 Database.insert(Array of microOperations)

12 Output.setData(convertToProperty(Input.instance.get( ))

Pseudo code: Reader of EMS edge Module or Node

01 With DDS.interface.open_connector(Participant, SubParticipant) asconnector

02 Input=connector.get_input( )

03 Topic.register(“EMSData”)

04 Input.wait_for_publication(Topic)

05 While(true)

06 AERS_Algorithm( )

07 Input.wait( )

08 Input.take( )

09 (Command, Argument0, Argument1, Argument2)=Input.instance.get( )

10 EMSCommand(Command, Argument0, Argument1, Argument2)

In some embodiments, the above systems may be implemented as acloud-based computing environment, such as a virtual machine operatingwithin a computing cloud. In other embodiments, the computer system mayitself include a cloud-based computing environment, where thefunctionalities of the computer system are executed in a distributedfashion. Thus, the computer system, when configured as a computingcloud, may include pluralities of computing devices in various forms, aswill be described in greater detail below. In general, a cloud-basedcomputing environment is a resource that typically combines thecomputational power of a large grouping of processors (such as withinweb servers) and/or that combines the storage capacity of a largegrouping of computer memories or storage devices. Systems that providecloud-based resources may be utilized exclusively by their owners orsuch systems may be accessible to outside users who deploy applicationswithin the computing infrastructure to obtain the benefit of largecomputational or storage resources. The cloud may be formed, forexample, by a network of web servers that comprise a plurality ofcomputing devices, such as the computer system, with each server (or atleast a plurality thereof) providing processor and/or storage resources.These servers may manage workloads provided by multiple users (e.g.,cloud resource customers or other users). Typically, each user placesworkload demands upon the cloud that vary in real-time, sometimesdramatically. The nature and extent of these variations typicallydepends on the type of business associated with the user.

As will be appreciated by one skilled in the art, the present disclosuremay be embodied as a system, method or computer program product.Accordingly, the present disclosure may take the form of an entirelyhardware embodiment, an entirely software embodiment (includingfirmware, resident software, micro-code, etc.) or an embodimentcombining software and hardware aspects that may all generally bereferred to herein as a “circuit,” “module” or “system.” Furthermore,the present disclosure may take the form of a computer program productembodied in one or more computer-readable medium(s) havingcomputer-readable program code embodied thereon.

Any combination of one or more computer-readable medium(s) may beutilized. The computer-readable medium may be a computer-readable signalmedium or a computer-readable storage medium. A computer-readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer-readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer-readable storagemedium may be any tangible medium that can contain or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer-readable signal medium may include a propagated data signalwith computer-readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer-readable signal medium may be any computer-readable medium thatis not a computer-readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device. Program codeembodied on a computer-readable medium may be transmitted using anyappropriate medium, including but not limited to wireless, wireline,optical fiber cable, RF, etc., or any suitable combination of theforegoing.

What is claimed is:
 1. A system to manage power consumption from a grid,comprising: a building switchgear; an energy storage system (ESS)coupled to the building switchgear to selectively provide power inresponse to a customer power demand to prevent a customer grid powerconsumption from spiking and peaking at grid imbalance highest cost onpeak times; an energy management system (EMS) to operate the ESS frombehind-the-meter; and a data distribution service (DDS) coupled to theEMS forming a DDS-EMS network to provide a global data space servicingEMS edge publishers and subscribers, wherein the EMS edge publishers andsubscribers looks for the DDS-EMS network and wherein each EMS edge nodereceives an acknowledgment and a signal on DDS-EMS network connection,wherein a trace module records and passes actions that occur to aRuntime Verification (RV) unit.
 2. The system of claim 1, comprising atrace layer that guarantees traceability and a response within apredetermined period.
 3. The system of claim 1, wherein the RV performsruntime monitoring based on Linear Temporal Logic (LTL).
 4. The systemof claim 1, wherein each edge publisher or subscriber performsread/write according to a quality of service (QoS) and wherein the QoSis communicated over a trace layer.
 5. The system of claim 4, whereinQoS comprises a predetermined operation time limit.
 6. The system ofclaim 4, wherein QoS comprises a 1000 ms operation time limit and theQoS is communicated to a trace module.
 7. The system, of claim 1,comprising an independent system operator (ISO) accepted meter coupledto the building switchgear, the ISO meter including a telemetry unit tocommunicate with an ISO.
 8. The system of claim 1, comprising a utilityrevenue grade meter coupled to the building switchgear.
 9. The system ofclaim 1, comprising an ESS controller to control operations of the ESSincluding a battery management system in each battery rack and a powerconversion system (PCS) coupled to the battery rack, and a battery firealarm system or fire suppression system.
 10. The system of claim 1,comprising AC and DC disconnect switches positioned between the grid andone or more power conversion systems.
 11. The system of claim 1,comprising code to profile Customer Electricity Usage, code to determineelectricity cost savings, and code to optimize resource capacity. 12.The system of claim 1, comprising code to determine a consumptionbehavior over a period of time to identify a Demand and Energy PeakUsage Pattern and Patterns during on-peak hours under Utility Tariffs.13. The system of claim 1, comprising code to find a highest peak (kW)during on-peak hours and code to find a lowest peak (kW) during on-peakhours.
 14. The system of claim 1, comprising code to calculate 95% oflowest peak and compensate with highest peak.
 15. A system to managepower consumption from a grid, comprising: a building switchgear; anindependent system operator (ISO) accepted meter coupled to the buildingswitchgear, the ISO meter including a telemetry unit to communicate withan ISO; and an energy storage system (ESS) coupled to the buildingswitchgear, and an ISO or System Performance Meter, wherein the ESSselectively provides power in response to a customer power demand toprevent a customer grid power consumption from spiking and peaking atgrid imbalance highest cost on peak times; and a data distributionservice (DDS) coupled to the EMS forming a DDS-EMS network to provide aglobal data space servicing EMS edge publishers and subscribers, the DDSincluding a trace layer that guarantees traceability and a responsewithin a predetermined period, wherein the EMS edge publishers andsubscribers looks for the DDS-EMS network and wherein each EMS edge nodereceives an acknowledgment and a signal on DDS-EMS network connection,wherein a trace module records and passes actions that occur to aRuntime Verification (RV) unit.
 16. The system of claim 15, comprising aplurality of photovoltaic (PV) modules coupled to the ESS.
 17. Thesystem of claim 16, wherein the plurality of PV modules are connected toone or more PV combiners.
 18. The system of claim 17, comprising a DC-DCconverter coupled to the PV combiners, further comprising a plurality ofbattery combiners coupled to the DC-DC converter.